{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-10-13T10:31:07.923732Z",
     "start_time": "2025-10-13T10:31:05.891312Z"
    }
   },
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-14T01:21:26.789233Z",
     "start_time": "2025-10-14T01:21:26.762151Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df = pd.read_csv('../logs/mlm_training.csv')\n",
    "df"
   ],
   "id": "879ed220acafeaa5",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    epoch  train_loss  valid_loss            lr  mask_acc\n",
       "0       1    2.600144    1.975375  5.000000e-07  0.422958\n",
       "1       2    1.827481    1.734003  1.000000e-06  0.463168\n",
       "2       3    1.586404    1.600356  1.500000e-06  0.495572\n",
       "3       4    1.471175    1.423578  2.000000e-06  0.543338\n",
       "4       5    1.333249    1.240133  2.500000e-06  0.581535\n",
       "5       6    1.185279    1.122091  3.000000e-06  0.601400\n",
       "6       7    1.107618    1.069713  3.500000e-06  0.609210\n",
       "7       8    1.063677    1.039960  4.000000e-06  0.615425\n",
       "8       9    1.033667    1.016082  4.500000e-06  0.623881\n",
       "9      10    1.006157    0.986564  5.000000e-06  0.637115\n",
       "10     11    0.976985    0.949702  5.500000e-06  0.653883\n",
       "11     12    0.946609    0.920162  6.000000e-06  0.666453\n",
       "12     13    0.917012    0.901379  6.500000e-06  0.674092\n",
       "13     14    0.889936    0.870923  7.000000e-06  0.685209\n",
       "14     15    0.863357    0.857089  7.500000e-06  0.689824\n",
       "15     16    0.837459    0.827438  8.000000e-06  0.699880\n",
       "16     17    0.811114    0.823921  8.500000e-06  0.703304\n",
       "17     18    0.784405    0.796777  9.000000e-06  0.712893\n",
       "18     19    0.757035    0.780653  9.500000e-06  0.719571\n",
       "19     20    0.729309    0.742730  1.000000e-05  0.732148\n",
       "20     21    0.699944    0.728026  9.944444e-06  0.739747\n",
       "21     22    0.668265    0.662091  9.888889e-06  0.763220\n",
       "22     23    0.634900    0.610995  9.833333e-06  0.781131\n",
       "23     24    0.602172    0.567238  9.777778e-06  0.796193\n",
       "24     25    0.569793    0.517811  9.722222e-06  0.813594\n",
       "25     26    0.538675    0.469968  9.666667e-06  0.831677\n",
       "26     27    0.507738    0.428374  9.611111e-06  0.848269\n",
       "27     28    0.476460    0.381638  9.555556e-06  0.866397\n",
       "28     29    0.447306    0.357358  9.500000e-06  0.874795\n",
       "29     30    0.419143    0.331525  9.444444e-06  0.884775\n",
       "30     31    0.391098    0.304306  9.388889e-06  0.894261\n",
       "31     32    0.362973    0.282214  9.333333e-06  0.901864\n",
       "32     33    0.338353    0.257216  9.277778e-06  0.909831\n",
       "33     34    0.316653    0.243024  9.222222e-06  0.914511\n",
       "34     35    0.298399    0.231046  9.166667e-06  0.918456\n",
       "35     36    0.283007    0.221447  9.111111e-06  0.921249\n",
       "36     37    0.270401    0.214255  9.055556e-06  0.923573\n",
       "37     38    0.260259    0.207159  9.000000e-06  0.925768\n",
       "38     39    0.251281    0.202263  8.944444e-06  0.927181\n",
       "39     40    0.244176    0.197788  8.888889e-06  0.928281\n",
       "40     41    0.237989    0.194871  8.833333e-06  0.929641\n",
       "41     42    0.232720    0.192963  8.777778e-06  0.930121\n",
       "42     43    0.228486    0.190335  8.722222e-06  0.930963\n",
       "43     44    0.224423    0.187854  8.666667e-06  0.931674"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>lr</th>\n",
       "      <th>mask_acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2.600144</td>\n",
       "      <td>1.975375</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.422958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.827481</td>\n",
       "      <td>1.734003</td>\n",
       "      <td>1.000000e-06</td>\n",
       "      <td>0.463168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.586404</td>\n",
       "      <td>1.600356</td>\n",
       "      <td>1.500000e-06</td>\n",
       "      <td>0.495572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.471175</td>\n",
       "      <td>1.423578</td>\n",
       "      <td>2.000000e-06</td>\n",
       "      <td>0.543338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1.333249</td>\n",
       "      <td>1.240133</td>\n",
       "      <td>2.500000e-06</td>\n",
       "      <td>0.581535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>1.185279</td>\n",
       "      <td>1.122091</td>\n",
       "      <td>3.000000e-06</td>\n",
       "      <td>0.601400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>1.107618</td>\n",
       "      <td>1.069713</td>\n",
       "      <td>3.500000e-06</td>\n",
       "      <td>0.609210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>1.063677</td>\n",
       "      <td>1.039960</td>\n",
       "      <td>4.000000e-06</td>\n",
       "      <td>0.615425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1.033667</td>\n",
       "      <td>1.016082</td>\n",
       "      <td>4.500000e-06</td>\n",
       "      <td>0.623881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1.006157</td>\n",
       "      <td>0.986564</td>\n",
       "      <td>5.000000e-06</td>\n",
       "      <td>0.637115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>0.976985</td>\n",
       "      <td>0.949702</td>\n",
       "      <td>5.500000e-06</td>\n",
       "      <td>0.653883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>0.946609</td>\n",
       "      <td>0.920162</td>\n",
       "      <td>6.000000e-06</td>\n",
       "      <td>0.666453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0.917012</td>\n",
       "      <td>0.901379</td>\n",
       "      <td>6.500000e-06</td>\n",
       "      <td>0.674092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0.889936</td>\n",
       "      <td>0.870923</td>\n",
       "      <td>7.000000e-06</td>\n",
       "      <td>0.685209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0.863357</td>\n",
       "      <td>0.857089</td>\n",
       "      <td>7.500000e-06</td>\n",
       "      <td>0.689824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>0.837459</td>\n",
       "      <td>0.827438</td>\n",
       "      <td>8.000000e-06</td>\n",
       "      <td>0.699880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0.811114</td>\n",
       "      <td>0.823921</td>\n",
       "      <td>8.500000e-06</td>\n",
       "      <td>0.703304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>0.784405</td>\n",
       "      <td>0.796777</td>\n",
       "      <td>9.000000e-06</td>\n",
       "      <td>0.712893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0.757035</td>\n",
       "      <td>0.780653</td>\n",
       "      <td>9.500000e-06</td>\n",
       "      <td>0.719571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>0.729309</td>\n",
       "      <td>0.742730</td>\n",
       "      <td>1.000000e-05</td>\n",
       "      <td>0.732148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>0.699944</td>\n",
       "      <td>0.728026</td>\n",
       "      <td>9.944444e-06</td>\n",
       "      <td>0.739747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>0.668265</td>\n",
       "      <td>0.662091</td>\n",
       "      <td>9.888889e-06</td>\n",
       "      <td>0.763220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>0.634900</td>\n",
       "      <td>0.610995</td>\n",
       "      <td>9.833333e-06</td>\n",
       "      <td>0.781131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>0.602172</td>\n",
       "      <td>0.567238</td>\n",
       "      <td>9.777778e-06</td>\n",
       "      <td>0.796193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>0.569793</td>\n",
       "      <td>0.517811</td>\n",
       "      <td>9.722222e-06</td>\n",
       "      <td>0.813594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>0.538675</td>\n",
       "      <td>0.469968</td>\n",
       "      <td>9.666667e-06</td>\n",
       "      <td>0.831677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0.507738</td>\n",
       "      <td>0.428374</td>\n",
       "      <td>9.611111e-06</td>\n",
       "      <td>0.848269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0.476460</td>\n",
       "      <td>0.381638</td>\n",
       "      <td>9.555556e-06</td>\n",
       "      <td>0.866397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>0.447306</td>\n",
       "      <td>0.357358</td>\n",
       "      <td>9.500000e-06</td>\n",
       "      <td>0.874795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0.419143</td>\n",
       "      <td>0.331525</td>\n",
       "      <td>9.444444e-06</td>\n",
       "      <td>0.884775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>0.391098</td>\n",
       "      <td>0.304306</td>\n",
       "      <td>9.388889e-06</td>\n",
       "      <td>0.894261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>0.362973</td>\n",
       "      <td>0.282214</td>\n",
       "      <td>9.333333e-06</td>\n",
       "      <td>0.901864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>0.338353</td>\n",
       "      <td>0.257216</td>\n",
       "      <td>9.277778e-06</td>\n",
       "      <td>0.909831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>0.316653</td>\n",
       "      <td>0.243024</td>\n",
       "      <td>9.222222e-06</td>\n",
       "      <td>0.914511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>0.298399</td>\n",
       "      <td>0.231046</td>\n",
       "      <td>9.166667e-06</td>\n",
       "      <td>0.918456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>0.283007</td>\n",
       "      <td>0.221447</td>\n",
       "      <td>9.111111e-06</td>\n",
       "      <td>0.921249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>0.270401</td>\n",
       "      <td>0.214255</td>\n",
       "      <td>9.055556e-06</td>\n",
       "      <td>0.923573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>0.260259</td>\n",
       "      <td>0.207159</td>\n",
       "      <td>9.000000e-06</td>\n",
       "      <td>0.925768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>0.251281</td>\n",
       "      <td>0.202263</td>\n",
       "      <td>8.944444e-06</td>\n",
       "      <td>0.927181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>0.244176</td>\n",
       "      <td>0.197788</td>\n",
       "      <td>8.888889e-06</td>\n",
       "      <td>0.928281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>0.237989</td>\n",
       "      <td>0.194871</td>\n",
       "      <td>8.833333e-06</td>\n",
       "      <td>0.929641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>0.232720</td>\n",
       "      <td>0.192963</td>\n",
       "      <td>8.777778e-06</td>\n",
       "      <td>0.930121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>0.228486</td>\n",
       "      <td>0.190335</td>\n",
       "      <td>8.722222e-06</td>\n",
       "      <td>0.930963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>0.224423</td>\n",
       "      <td>0.187854</td>\n",
       "      <td>8.666667e-06</td>\n",
       "      <td>0.931674</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-14T01:21:30.680705Z",
     "start_time": "2025-10-14T01:21:30.660435Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.style.use('ggplot')\n",
    "plt.rcParams['font.family'] = 'DejaVu Sans'\n",
    "plt.rcParams['figure.dpi'] = 120"
   ],
   "id": "290bcd566798930c",
   "outputs": [],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-14T01:21:44.860253Z",
     "start_time": "2025-10-14T01:21:44.333028Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# loss对比图\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(df['epoch'], df['train_loss'], marker='o', markersize=6,\n",
    "         label='Train Loss', color='#2c7bb6', linewidth=2, linestyle='-')\n",
    "plt.plot(df['epoch'], df['valid_loss'], marker='s', markersize=6,\n",
    "         label='Valid Loss', color='#d7191c', linewidth=2, linestyle='--')\n",
    "plt.title('Loss Comparison (Train vs Validation)', fontsize=14, pad=15)\n",
    "plt.xlabel('Epoch', fontsize=12)\n",
    "plt.ylabel('Loss', fontsize=12)\n",
    "plt.xticks(df['epoch'])\n",
    "plt.grid(True, linestyle=':', alpha=0.7)\n",
    "plt.legend(fontsize=10, framealpha=1)\n",
    "plt.xlim(0, 46)\n",
    "plt.ylim(0.07, 3.00)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.tight_layout()\n",
    "plt.savefig('../logs/mini_mlm_loss.png')\n",
    "plt.show()"
   ],
   "id": "79b6880853943d6c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x720 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-29T08:26:12.866837Z",
     "start_time": "2025-09-29T08:26:12.072296Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# valid mask acc图\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(df['epoch'], df['mask_acc'], marker='o', markersize=8,\n",
    "         label='Train Loss', color='#2c7bb6', linewidth=2, linestyle='-')\n",
    "plt.title('MASK ACC', fontsize=14, pad=15)\n",
    "plt.xlabel('Epoch', fontsize=12)\n",
    "plt.ylabel('ACC', fontsize=12)\n",
    "plt.xticks(df['epoch'])\n",
    "plt.grid(True, linestyle=':', alpha=0.7)\n",
    "plt.legend(fontsize=10, framealpha=1)\n",
    "plt.xlim(0, 44)\n",
    "plt.ylim(0.2, 0.9)\n",
    "plt.gca().spines['top'].set_visible(False)\n",
    "plt.gca().spines['right'].set_visible(False)\n",
    "plt.tight_layout()\n",
    "plt.savefig('../logs/mini_mask_acc.png')\n",
    "plt.show()"
   ],
   "id": "9d9571cdefd54da8",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x720 with 1 Axes>"
      ],
      "image/png": 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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 9
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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    "version": 2
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